Improved Small Object Detection in Remote Sensing Images Based on YOLOv7
Due to factors such as small target size,obscure visual information and complex and changeable background,the traditional detection methods have limitations in terms of accuracy and robustness,and are prone to miss detection and false detection.In this paper,an improved YOLOv7 target detection algorithm is proposed.SIoU is used as a loss function to improve the positioning accuracy of the target detection frame,so as to improve the accuracy and robustness of the detection.At the same time,by applying CNeB module to the feature fusion process,the spatial interaction between features is enhanced,and the detection performance of small targets is further improved.In addition,in order to better capture the detail features of small and medium-sized targets in remote sensing images,the MPCA module is designed by using the CA attention mechanism to realize the adaptive adjustment of feature maps to improve the representation ability.In the experimental part,a large number of experimental evaluations are carried out using classical remote sensing image dataset.The experimental results show that the proposed method based on SIoU,CNeB module and CA attention mechanism has an average accuracy of 96.8%on the four-class mean on the RSOD dataset,which is 2.5%higher than that of the original YOLOv7,and effectively improves the detection accuracy of small targets in remote sensing images.